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Problem Structuring and Analytical Frameworks Questions

The ability to convert ambiguous business problems into clear, testable, and actionable analytical questions and frameworks. Candidates should demonstrate how to clarify the decision to be informed and success metrics, break large problems into smaller components, and organize thinking using hypothesis driven approaches, issue trees, or mutually exclusive and collectively exhaustive groupings. This includes generating hypotheses, identifying key drivers and uncertainties, specifying required data sources and any necessary transformations, choosing analytical methods, estimating effort and impact, sequencing and prioritizing analyses or experiments, and planning next steps that produce evidence to guide decisions. Interviewers also assess evaluation of trade offs, recommending a decision with a clear rationale, effective communication of structure and findings, and comfort operating with incomplete information. The scope includes applying general case structuring as well as specialized frameworks such as growth funnel analysis that maps acquisition, activation, revenue, retention, and referral, audience segmentation and competitive assessment frameworks, content and channel strategy, and operational step by step approaches. For more junior candidates the emphasis is on clear structure, systematic thinking, strong rationale, and prioritized next steps rather than exhaustive optimization.

HardTechnical
0 practiced
Case: Monthly Active Users (MAU) dropped 12% in the last quarter. As the lead data scientist, outline an end-to-end investigation and action plan: frame the decision(s) to be made, generate hypotheses, list required datasets and transformations, propose analytical methods (diagnostics, causal tests, experiments), sequence and prioritize tasks, estimate effort and impact, and state what recommendation(s) you might make under different findings.
MediumTechnical
0 practiced
Design an A/B test to increase onboarding activation for a SaaS product. Define the primary metric, two supporting metrics, sample size considerations, minimum detectable effect (MDE), experiment length, and guardrail metrics. Explain assumptions and how you'd handle early stopping requests from stakeholders.
MediumTechnical
0 practiced
Revenue has declined in several regions. You have two analysts and a two-week window. Describe a prioritized plan of analyses: what diagnostics you run in day 1–2, which regional splits and cohorts to inspect, what quick experiments or dashboards to build, and what you'd escalate to leadership if you find major anomalies.
MediumTechnical
0 practiced
You need to recommend an audience segmentation strategy to power personalized content recommendations. Describe candidate segmentation approaches (rule-based, clustering, supervised propensity), validation metrics (e.g., lift in CTR, retention), sample size/holdout needs, and how you'd move from segments to live personalization tests.
EasyTechnical
0 practiced
You are a data scientist at an e-commerce company. The VP reports that the conversion rate 'fell last month' but gives no additional detail. Convert this ambiguous statement into a clear, testable analytical brief: define the decision(s) to be informed, propose a primary success metric and two guardrail metrics, and outline four initial analyses you would run to triage the issue.

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